Personalized fall detector

ABSTRACT

A method and system for training a fall detection classifier using subject-specific movement data. A subject sets a preferred non-fall detection rate. Movement data responsive to a subject&#39;s movements during everyday activities are obtained over a predetermined data collection period. For each detected event in the movement data, values for one or more parameters that may (together or individually) indicate a fall are obtained. The obtained values are used to generate a subject-specific non-fall detection rate function. This non-fall detection rate function is used to derive a threshold value, in reference to the subject-set preferred non-fall detection rate, to distinguish fall events from non-fall events.

FIELD OF THE INVENTION

The invention relates to the field of fall detection in PersonalEmergency Response Systems.

BACKGROUND OF THE INVENTION

Personal Emergency Response Systems (PERS) enable elderly and disabledpeople to live independently by summoning immediate help when an adverseevent, such as a fall, occurs. The use of such systems allows people whowould otherwise require round-the-clock care to live in their own homes,and reduces their care costs.

Some PERS systems rely on subject input, such as a button, to raise analarm when assistance is required, but this means that a subject isunable to access immediate medical help in the case of the subject'sbecoming unconscious as a result of a fall, when such help is mostcritical. There is also a risk that the subject may fall in such a waythat, while remaining conscious, he/she is unable to activate the alarm.

For these reasons, automatic fall detectors have been developed that cansummon help without needing subject input. These detectors generallycome in the form of wearable devices that contain sensors to monitorsubjects' movements and processors that decide whether a detectedmovement is the result of a fall. These devices are often located aroundthe neck, at the waist or around the wrist, but other locations are alsoconceivable, including such locations as ears (for example, in hearingaids). Wrist-located devices are becoming increasingly popular assmartwatches, on which fall detection apps may be installed, become morewidely used.

Current automatic fall detectors are not able to distinguish withcomplete accuracy between falls and movements that occur during everydayactivities. Fall detection accuracy is particularly low in wrist-locateddevices.

In order to avoid failing to detect genuine falls, fall detectors areconfigured to have a sufficiently high false alarm rate (the rate ofnon-fall events classified as fall events) to minimize the number offalls that are not detected as such. A high false alarm ratenecessitates the use of a cancel function to avoid summoning helpneedlessly; subjects can activate this function when the fall detectorincorrectly detects a fall. However, some subjects may find the cancelfunction difficult to use, become panicked when a false alarm occurs andforget to activate the cancel function, or accidentally activate thecancel function when a genuine fall has occurred.

There is therefore a need for a fall detector with improved accuracy,such that false alarms are unlikely enough that a cancel function is notrequired.

SUMMARY OF THE INVENTION

The invention is defined by the claims.

According to examples in accordance with an aspect of the invention,there is provided a computer-based method of training a classifier todistinguish between a fall event and a non-fall event of a subject.

The method comprises: obtaining movement data responsive to thesubject's movement during everyday activities over a predetermined datacollection period; detecting one or more events in the movement data;obtaining a value for each of a one or more parameters from the movementdata at the time of the event for each event that occurs during thepredetermined data collection period; generating a non-fall eventprobability distribution by calculating a probability distribution for acombination of the one or more parameters using the obtained values foreach of the one or more parameters, wherein the combination of the oneor more parameters is capable of indicating the probability of a fall;obtaining a fall event probability distribution using the non-fall eventprobability distribution; and obtaining a threshold value for the fallevent probability distribution that distinguishes between a fall eventand a non-fall event.

This method uses a subject-specific probability distribution that anevent is not a fall in order to distinguish between a fall and anon-fall event with greater accuracy than methods that use a probabilitydistribution that an event is (not a) fall based on a populationaverage, as different subjects will exhibit different typical movementbehaviors.

The subject's movements are detected during everyday activities over aninitial data collection period; events are recorded based on themovement data, and one or more values for parameters that could affectthe probability that an event is a fall, such as height change andorientation, are computed. Once the initial data collection period iscompleted, for example by a time interval having been passed or by aminimum number of events being collected, these values are used todetermine a subject-specific probability distribution for non-fallevents.

The subject-specific probability distribution for non-fall events isused to obtain or derive a fall event probability distribution.

In subsequent processing for detection of a fall event, a calculatedfall event probability for the combination of one or more values (forthe one or more parameters) for a detected event can be compared with athreshold value in order to decide whether the detected event is a fallor a non-fall event.

Events may be detected by setting at least one of the one or moreparameters as a trigger parameter and defining an event as occurring ata time at which a value of the at least one trigger parameter exceeds orfalls below a corresponding predetermined trigger threshold value. Othermethods of detecting an event will be apparent to the skilled person,e.g. by processing a chunk of movement data using a machine-learningmethod.

The proposed embodiments enable a fall detector to be personalized tothe movement of a subject, i.e. to better distinguish between a fall anda non-fall of the subject. In particular, a fall detection probabilitydistribution (that is used to distinguish between a fall and a non-fall)is personalized to a user. This reduces the likelihood of false alarmsbeing generated when a subject has not actually fallen.

In some embodiments, the one or more parameters comprises a plurality ofparameters, so that the combination of one or more parameters comprisesa combination of a plurality of parameters.

In some embodiments, the calculated probability distribution for thecombination of the one or more parameters is calculated by determining aprobability distribution for each of the one or more parameters, anddefining the calculated probability distribution for the combination ofthe one or more parameters as the product of the determined probabilitydistributions. Put another way, the logarithm of the probabilitydistribution (in an arbitrary base) for the combination of the one ormore parameters is computed as the sum of the logarithms of thedetermined individual probability distributions (in the same base).

The step of obtaining a fall event probability distribution using thenon-fall event probability distribution may comprise calculating thefall event probability distribution using the non-fall event probabilitydistribution. The step of processing the non-fall probabilitydistribution to obtain the fall event probability distribution maycomprise processing the non-fall event probability distribution suchthat there is a high fall event probability for values at which thenon-fall event probability is low and vice versa. For example, the fallevent probability distribution may be the inverse or compliment of thenon-fall event probability distribution.

In another example, the step of obtaining the fall event probabilitydistribution may comprise dividing a predetermined estimation of thefall event probability distribution by the non-fall event probabilitydistribution. The quotient may be normalized to derive a fall eventprobability distribution, or may by itself effectively form a fall eventprobability distribution.

Thus, a probability distribution for fall events is generated by usingthe idea that events that are likely to be non-fall events are unlikelyto be fall events. This avoids the need for directly obtaining reliablefall data of a user, which are difficult and time-consuming to obtain.

In some embodiments, the step of obtaining the fall event probabilitydistribution comprises processing only part of the non-fall eventprobability distribution, which part of the non-fall probabilitydistribution to be processed being determined using a predeterminedestimation of a fall event probability distribution.

This recognizes that there may be some values for the combination ofvalues of the one or more parameters for an event at which the non-fallevent probability is low and at which it is unlikely that the event is afall, according to an estimated fall event probability distribution.Using an estimated fall event distribution to limit the part of thenon-fall event probability distribution that is processed to obtain afall event probability distribution excludes such values, which reducesthe number of false alarms detected, that is, the number of non-fallevents that are determined to be falls.

The part of the non-fall event probability distribution processed toobtain the fall event probability distribution may comprise the non-fallevent probability distribution for values of the combination of the oneor more parameters for which the predetermined estimation of a fallevent probability exceeds a predetermined minimum value.

The part of the non-fall event probability distribution may comprise thenon-fall event probability distribution for values of the combination ofthe one or more parameters lying between the combination of the one ormore parameters having a maximum value in the non-fall event probabilitydistribution and the combination of the one or more parameters having amaximum value in the predetermined estimation of a fall eventprobability distribution.

The fall event probability distributed may be calculated by computing alikelihood ratio, defined by dividing the predetermined estimation of afall event probability distribution by the determined non-fall eventprobability distribution.

In this way, the likelihood ratio test is applied in order to determinethe fall event probability distribution. The likelihood ratio test hasbeen shown, by the Neyman-Pearson theorem, to be the most powerful testfor determining whether or not an event is a fall for a given falsealarm rate. The most powerful test is the test with the lowestprobability of determining a genuine fall event to be a non-fall event.

The threshold value for the fall-event probability distribution may becomputed using the non-fall event probability distribution and the fallevent probability distribution. In other examples, the threshold valuemay be set as a predetermined value.

In other examples, the threshold value may be set as the value at whichthe false alarm rate for the subject, defined as the rate at whichnon-fall events are determined to be fall events, is equal to apredetermined rate. In this way, the threshold value may be personalizedto the subject to ensure that the false alarm rate for that subject isat an acceptable level.

In other examples, the threshold value is a predetermined thresholdvalue, e.g. one that may have been used if a population-based fall eventprobability distribution were used.

According to examples in accordance with an aspect of the invention,there is provided a computer program comprising code means forimplementing the method of any previously described method when saidprogram is run on a processing system.

According to a concept of the invention, there is provided a processingsystem adapted to: obtain movement data responsive to the subject'smovement during everyday activities over a predetermined data collectionperiod; detect one or more events in the movement data; obtain a valuefor each of one or more parameters from the movement data at the time ofthe event for each event that occurs during the predetermined datacollection period; a non-fall event probability distribution bycalculating a probability distribution for a combination of one or moreparameters using the obtained values for each of one or more parameters,wherein the combination of the one or more parameters is capable ofindicating the probability of a fall; obtain a fall event probabilitydistribution using the non-fall event probability distribution; andobtain a threshold value for the fall event probability distributionthat distinguishes between a fall event and a non-fall event.

The processing system may be adapted to obtain the fall eventprobability distribution by processing the non-fall event probabilitydistribution, such that there is a high fall event probability forvalues at which the non-fall event probability is low and vice versa.

There is also proposed a system for detecting a fall of a subject,comprising one or more sensors for obtaining movement data responsive tothe subject's movement, and the processing system described above,further configured to: receive the movement data from the one or moresensors; detect events in the movement data after the predetermined datacollection period has elapsed; and classify each detected event as afall event or a non-fall event by comparing the fall event probabilityfor the combination of obtained values for the one or more parameters,for the event, with the threshold value.

The processing system may, when the threshold value corresponds to apredetermined false alarm rate, be further configured to determine afalse alarm probability, defined as the quotient between thepredetermined false alarm rate and a rate at which events are detectedfor the subject, and the system for detecting a fall of a subject mayfurther comprise a user interface configured to provide a cancelfunction, which, when activated by the subject, instructs the processingsystem to re-classify a detected fall event as a non-fall event, whereinthe user interface is configured to display the predetermined falsealarm rate and/or the false alarm probability, and optionally to receivea user instruction to disable the cancel function, and the userinterface is configured to selectively disable the cancel function,after the predetermined data collection period has elapsed, responsiveto the value of the determined false alarm probability and/or a userinstruction.

Disabling the cancel function when the system is judged to classifyevents with sufficient accuracy reduces interaction complexity andincreases subject safety by preventing the cancel function from beingaccidentally activated in the case of a real fall event.

According to one aspect of the invention there is provided acomputer-based method (100) of training a classifier to distinguishbetween a fall event and a non-fall event of a subject, thecomputer-based method comprising: obtaining (110) movement data (615)responsive to the subject's movement during everyday activities over apredetermined data collection period; detecting (120) one or more eventsin the movement data (615); obtaining (130) a value for each of a one ormore parameters from the movement data (615) at the time of the eventfor each event that occurs during the predetermined data collectionperiod; generating (140) a non-fall event probability distribution (200)by calculating a probability distribution for a combination of the oneor more parameters using the obtained values for each of the one or moreparameters, wherein the combination of the one or more parameters iscapable of indicating the probability of a fall; obtaining (150) a fallevent probability distribution (300, 400, 500) using the non-fall eventprobability distribution; and obtaining a threshold value for the fallevent probability distribution that distinguishes between a fall eventand a non-fall event.

In one embodiment, the calculated probability distribution for thecombination of the one or more parameters is calculated by: determininga probability distribution for each of the one or more parameters; anddefining the calculated probability distribution for the combination ofthe one or more parameters as the product of the determined probabilitydistributions.

In one embodiment the step of obtaining the fall event probabilitydistribution (300, 500) comprises processing the non-fall eventprobability distribution (200), such that there is a high fall eventprobability for values at which the non-fall event probability is lowand vice versa.

In one embodiment, the step of obtaining the fall event probabilitydistribution comprises calculating the inverse or complement of thenon-fall event probability distribution (200).

In one embodiment, the step of obtaining the fall event probabilitydistribution comprises dividing a predetermined estimation of a fallevent probability distribution by the non-fall event probabilitydistribution.

In one embodiment, the step of obtaining the fall event probabilitydistribution (300, 500) comprises processing only part of the non-fallevent probability distribution (200), which part of the non-fallprobability distribution (200) to be processed being determined using apredetermined estimation of a fall event probability distribution (400).

In one embodiment, the part of the non-fall event probabilitydistribution (200) comprises the non-fall event probability distribution(200) for values of the combination of the one or more parameters forwhich the predetermined estimation of a fall event probability exceeds apredetermined minimum value.

In one embodiment, the part of the non-fall event probabilitydistribution (200) comprises the non-fall event probability distribution(200) for values of the combination of the one or more parameters lyingbetween the combination of the one or more parameters having a maximumvalue in the non-fall event probability distribution (200) and thecombination of the one or more parameters having a maximum value in thepredetermined estimation of a fall event probability distribution (400).

In one embodiment, the step of obtaining a threshold value for the fallevent probability distribution comprises processing (160) the non-fallevent probability distribution (200) and the fall event probabilitydistribution (300, 400, 500) to determine the threshold value (624).

In one embodiment, the threshold value (624) is set as the value atwhich the false alarm rate for the subject, defined as the rate at whichnon-fall events are determined to be fall events, is equal to apredetermined rate.

According to another aspect of the invention there is provided acomputer program comprising code means for implementing theaforementioned methods when said program is run on a processing system.

According to yet another aspect of the invention is provided aprocessing system (620) adapted to: obtain (110) movement data (615)responsive to the subject's movement during everyday activities over apredetermined data collection period; detect (120) one or more events inthe movement data (615); obtain (130) a value for each of one or moreparameters from the movement data (615) at the time of the event foreach event that occurs during the predetermined data collection period;generate (140) a non-fall event probability distribution (200) bycalculating a probability distribution for a combination of the one ormore parameters using the obtained values for each of one or moreparameters, wherein the combination of the one or more parameters iscapable of indicating the probability of a fall; obtain (150) a fallevent probability distribution (300, 400, 500) using the non-fall eventprobability distribution; and obtain a threshold value for the fallevent probability distribution that distinguishes between a fall eventand a non-fall event.

In one embodiment, the processing system (620) is adapted to obtain thefall event probability distribution (300, 500) by processing thenon-fall event probability distribution (200), such that there is a highfall event probability for values at which the non-fall eventprobability is low and vice versa According to yet another aspect of theinvention there is provided a system (600) for detecting a fall of asubject, comprising: one or more sensors (610) for obtaining movementdata (615) responsive to the subject's movement; and the processingsystem (620) of claim 12 or 13, further configured to: receive themovement data (615) from the one or more sensors (610); detect events inthe movement data (615) after the predetermined data collection periodhas elapsed; and classify each detected event as a fall event or anon-fall event by comparing the fall event probability for thecombination of obtained values, for the event, with the threshold value(624).

In one embodiment, the threshold value (624) is configured to correspondto a predetermined false alarm rate, the processing system (620) isconfigured to determine a false alarm probability using the movementdata, and wherein the system (600) further comprises: a user interface(630) configured to provide a cancel function (635), which, whenactivated by the subject, instructs the processing system (620) tore-classify a detected fall event as a non-fall event, wherein the userinterface is configured to display the predetermined false alarm rateand/or the false alarm probability, and optionally to receive a userinstruction to disable the cancel function, and the user interface isconfigured to selectively disable the cancel function (635), after thepredetermined data collection period has elapsed, responsive to thevalue of the determined false alarm probability and/or a userinstruction.

According to yet another aspect of the invention there is provided acomputer-based method (160) of adapting a classifier to distinguishbetween a fall event and a non-fall event of a subject, thecomputer-based method comprising: obtaining (110) movement data (615)responsive to the subject's movement during everyday activities over apredetermined data collection period; detecting (120) one or more eventsin the movement data (615); obtaining (130) a value for each of a one ormore parameters from the movement data (615) at the time of the eventfor each event that occurs during the predetermined data collectionperiod; obtaining a fall event probability distribution that ispredetermined or based on a combination of the one or more parameters;determining a non-fall detection rate function based on the obtainedfall event probability distribution determining a threshold value fromthe non-fall detection rate function, where the non-fall detection rateis below a preset value.

In one embodiment, the step of obtaining a fall event probabilitydistribution is based on a combination of the one or more parametersusing the obtained values for each of the one or more parameters; andthe step of determining a non-fall detecting rate function is based onthe obtained fall event probability distribution and the obtained valuesfor each of the one or more parameters.

In one embodiment, the step of determining a non-fall detection ratefunction also takes into account the duration of a predetermined datacollection period.

In an embodiment, the step of determining the threshold value is adaptedbased on a difference or ratio between a set false alarm rate and anobserved false alarm rate.

In an embodiment, the step of determining the threshold value is adaptedbased on the relative difference between current false alarm rate and aset false alarm rate.

According to yet another aspect of the invention there is provided afall detector system for detecting a fall of a subject, the systemcomprising: a user interface having an input for enabling a user to seta false alarm rate reference value; a threshold determination subsystemis configured to execute any of the aforementioned computer-basedmethods for determining a threshold value corresponding to the falsealarm rate reference value; and wherein the fall detector is configuredto use the determined threshold.

In an embodiment, the false alarm rate reference value that is enteredby the user is relative to an existing value.

According to yet another aspect of the invention there is provided amethod for training a fall detection classifier to distinguish betweenfall events and non-fall events for a user by using subject-specificmovement data, the method comprising: setting a preferred non-falldetection rate for the user; obtaining the subject-specific movementdata by monitoring a subject's movements during everyday activities overa predetermined data collection period, wherein for each detected eventin the movement data, values for one or more parameters indicating afall are obtained; generating a subject-specific non-fall detection ratefunction based on the obtained values; and determining a threshold valueusing the non-fall detection rate function and the non-fall detectionrate set by the user to distinguish fall events from non-fall events.

These and other aspects of the invention will be apparent from andelucidated with reference to the embodiments described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the invention, and to show more clearlyhow it may be carried into effect, reference will now be made, by way ofexample only, to the accompanying drawings, in which:

FIG. 1 is a flow diagram of a method of training a classifier todistinguish between a fall event and a non-fall event of a subject,according to an embodiment of the invention.

FIG. 2 is a graph showing an example of a non-fall event probabilitydistribution.

FIG. 3 is a graph showing an example of a fall event probabilitydistribution generated using a method according to an embodiment of theinvention.

FIG. 4 is a graph showing an example of a non-fall event probabilitydistribution and an example of a predetermined estimation of a fallevent probability distribution.

FIG. 5 is a graph showing another example of a fall event probabilitydistribution generated using a method according to an embodiment of theinvention.

FIG. 6 is a diagram of a system for detecting a fall of a subjectaccording to the invention.

FIG. 7 shows a system for adapting the detection threshold based on auser setting the false alarm rate.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The invention will be described with reference to the Figures.

It should be understood that the detailed description and specificexamples, while indicating exemplary embodiments of the systems andmethods, are intended for the purposes of illustration only and are notintended to limit the scope of the invention. These and other features,aspects and advantages of the systems and methods of the presentinvention will become better understood from the following description,appended claims and accompanying drawings. It should be understood thatthe same reference numerals are used throughout the Figures to indicatethe same or similar parts.

Existing fall detection systems typically consist of two phases. In afirst phase, values for parameters that may indicate the likelihood of afall are obtained for an event from data from sensors such asaccelerometers and barometric sensors. Other sensors may includegyroscopes, magnetometers, ultrasound sensors, radar sensors, skinconductivity sensors and pulse (PPG) sensors, for example. Theparameters may include any of height, height change, orientation,orientation change, velocity, duration and impact (distance,physiological quantities). Events are typically defined using a trigger,which monitors at least one trigger parameter: an event is considered tohave occurred at the point at which the value of a trigger parameterexceeds or falls below a corresponding predetermined trigger thresholdvalue (or when a change of a value is greater than a predeterminedchange). For example, an event may be detected when the barometerindicates a height drop of more than 50 cm, or the accelerometerindicates an acceleration of more than 2 g.

In a second phase, a classifier (e.g. a processing device) takes one ormore of the obtained parameter values from the event and outputs a valueindicating or predicting whether or not the event is a fall. In somecases, the obtained parameter value(s) are used by the classifier tocalculate a probability that a fall has occurred, e.g. using a fallevent probability distribution, and the classifier applies a thresholdto the calculated probability to decide whether that probability ispredict, with a certain degree of accuracy, whether or not a fall hasoccurred.

According to a concept of the invention, there is proposed a method andsystem for training a fall detection classifier using subject-specificmovement data. Movement data responsive to a subject's movements duringeveryday activities are obtained over a predetermined data collectionperiod. For each detected event in the movement data, values for one ormore parameters that may (together or individually) indicate a fall areobtained. The obtained values are used to generate a subject-specificprobability distribution for non-fall events. A fall event probabilitydistribution is obtained using the non-fall event probabilitydistribution. This fall event probability distribution can then besubsequently processed, with reference to a threshold value, todistinguish fall events from non-fall events.

Embodiments are at least partly based on the realization that differentpeople exhibit different movement behaviors, and that a classifiertrained on subject-specific movement data can detect a subject's fallswith greater accuracy than a classifier trained using population data.

Illustrative embodiments may, for example, be employed in fall detectorsand/or fall detection systems in PERS systems at a subject's home or inspecialized accommodation such as Senior Living Facilities, in hospitalproducts, and/or using apps designed for smartwatches.

In any described embodiment, a probability distribution may be a jointprobability distribution or a multivariate probability distribution.This can be expressed as a cumulative distribution function or a jointprobability density function.

FIG. 1 illustrates a method 100 for training a classifier to distinguishbetween a fall event and a non-fall event of a subject, according to anembodiment of the invention.

The method 100 begins with step 110, in which movement data responsiveto the subject's movement during everyday activities, e.g. activities ofdaily living (ADL), are obtained over a predetermined data collectionperiod.

The movement data comprises sufficient information for a one or morepredefined parameters to be measured from the movement data, whereineach parameter, or a combination of the parameters, is capable ofindicating the probability of a fall. Examples of parameters capable ofindicating the probability of a fall include height, height change,orientation, orientation change, air pressure change, velocity,acceleration, duration and impact.

The movement data may comprise data from at least one suitable sensor.Examples of suitable sensors include accelerometers, barometric sensors,tilt switches, vibration sensors, gyroscopes, magnetometers, ultrasoundsensors, radar sensors, skin conductivity sensors and pulse(photoplethysmography, PPG) sensors. These data are collected as thesubject carries out everyday activities, such as standing up, sittingdown, lying down, bending over, walking and climbing stairs. Forwrist-located devices, the movements also include the many motionsrelated to activities such as pointing, waving, gesturing (duringconversations), (car) driving, washing dishes, doing laundry, and(vacuum) cleaning.

The predetermined data collection period may be a week, one or a fewdays, a month or some other suitable length of time. An initial shortdata collection period, for example, a day, may be used to obtain somemovement data, then data may continue to be collected over a longer datacollection period to refine the results.

The predetermined data collection period may also be the length of timetaken to detect a predetermined number of events, or the data collectionperiod may be defined by both a minimum number of events and a minimumduration. Thus, in some examples, the predetermined data collectionperiod may be defined as a period during which a predetermined number ofevents (described below) are detected. Other definitions are alsoconceivable, including user controlled methods.

The subject's movement data may be kept up-to-date, ensuring that fallevents continue to be accurately distinguished from non-fall events evenif the subject's typical movement behaviors change over time, byrepeating the method 100 at regular intervals, or by continuallycollecting movement data and using data from a moving window, forexample, data from the last week, in the remaining steps of the method100.

At step 120, the method comprises detecting one or more events in themovement data. An event may be defined by setting at least one of thepredefined parameter(s), or a combination of parameters, as a triggerparameter and defining an event as occurring at a time at which a valueof the at least one trigger parameter exceeds or falls below acorresponding predetermined trigger threshold value. The usedparameter(s) is (are) usable again in the next steps, but this is notessential.

The at least one parameter to be set as a trigger parameter may bepredefined. The at least one parameter may be a parameter that researchshows may be configured to detect all, or almost all, fall events whiledetecting a minimal number of non-fall events.

The at least one trigger parameter may be selected based on thecomputational complexity required to obtain a value of the triggerparameter. Examples of trigger parameters may include air pressurechange and acceleration size.

A suitable trigger threshold may be determined from literature, or fromsimulations of falls, and may be set to be a value for which all, oralmost all, fall events will pass. For example, an event may be detectedwhen the barometer indicates a height drop of more than 50 cm, or theaccelerometer indicates an acceleration of more than 2 g.

Other methods of detecting events, such as the use of a machine learningalgorithm to directly process chunks of movement data, will be apparentto the skilled person.

At step 130, the method comprises obtaining a value for each of one ormore parameters at the time of the event for each event that occursduring the predetermined data collection period. The one or moreparameters preferably comprises a plurality of parameters. This data maybe labelled “event information”.

In some examples, values are obtained for all of the predefinedparameters; in other examples, a selection process may be used to selecta subset of a plurality of predefined parameters that yields bestaccuracy for the subject. Examples of methods for selecting parameterswill be apparent to the skilled person, known in the art as “featureselection”, and may include forward search, backwards search and LASSO.

At step 140, the method comprises generating a non-fall eventprobability distribution (P_(ADL)) by calculating a probabilitydistribution for a combination of the one or more parameters using theevent information, i.e. using the obtained values for each of the one ormore parameters from all the events that occur during the predetermineddata collection period.

This probability distribution may be calculated by determining aprobability distribution for each of the one or more parameters, anddefining the calculated probability distribution for the combination ofthe one or more parameters as the product of the determined probabilitydistributions.

The log of the probability distribution for the combination of the oneor more parameters may therefore be found by calculating the sum of thelogs of the determined probability distributions.

Extreme values, due to, for example, the probability distribution for aparameter being close to zero, may be clipped.

In other examples, the non-fall event probability distribution may becalculated by first combining two or more parameters (e.g. into subsetsof parameters), before calculating the non-fall event probabilitydistribution for the combination.

The non-fall event probability distribution may be multivariate, i.e.form a joint probability distribution.

The probability distribution of a single parameter may be found bycurve-fitting to the collected set of values of that parameter andnormalizing the curve's integral to unit size. In some examples, allcollected values for a parameter may be used to generate the probabilitydistribution for the parameter, operating under the assumption that thenumber of falls that occur during the predetermined data collectionperiod will be low enough not to significantly affect the calculatedprobability distribution. In other examples, outliers may be removedbefore the curve-fitting, by, for example, excluding values from eventsthat have a high probability of being fall events, according to somepredetermined estimation of a fall event probability, and/or values fromevents that are defined as fall events based on subject input.

Since the non-fall event probability distribution is generated fromsubject-specific movement data rather than a population average, it maybe used to distinguish between fall events and non-fall events withgreater accuracy.

In some embodiments, a plurality of non-fall event probabilitydistributions (for different combinations of one or more parameters) maybe generated from the movement data. The plurality of non-fallprobability distributions may be employed in subsequent steps in placeof a single non-fall probability distribution.

At step 150, a fall event probability distribution (P_(D)) for thecombination of the one or more parameters is obtained (i.e. thoseparameters contained in the event information) using the non-fall eventprobability distribution.

Since the number of falls during the predetermined data collectionperiod may be low, a fall event probability distribution cannot bereliably obtained using or incorporating real, subject-specific falldata during the data collection period. That being said, in someexamples, real subject-specific data from any falls that are detectedafter the predetermined data collection period is complete may be usedto further refine the fall event probability distribution.

In preferred examples, the fall event probability distribution iscalculated by processing the non-fall event probability distribution.This process may follow the approach of anomaly detection, in whichevents outside the non-fall event probability distribution (anomalies)are classified as fall events.

A first example method of obtaining the fall event probabilitydistribution may comprise computing a likelihood ratio. This process maycomprise dividing a predetermined estimation of a fall event probabilitydistribution by the non-fall event probability distribution generatedfrom the subject's movement data. The likelihood ratio may then benormalized to formulate a probability distribution. Of course, in someexamples, the likelihood ratio may itself act as a probabilitydistribution (albeit

Another example method of obtaining a fall event probabilitydistribution comprises processing the non-fall event probabilitydistribution such that there is a high fall event probability for valuesat which the non-fall event probability is low and vice versa. A highprobability may be considered to be a probability above 0.5, where theprobability is on a scale of 0 to 1, while a low probability may beconsidered to be a probability below 0.5. These are purely exemplaryvalues, and the skilled person might consider other threshold values fordetermining whether a probability is “high” and “low”.

There are a number of ways in which the fall event probabilitydistribution may be defined in terms of the non-fall probabilitydistribution such that there is a high fall event probability for valuesat which the non-fall event probability is low and vice versa. Forexample, the fall event probability distribution may be defined as theinverse/reciprocal of the non-fall event probability distribution, theprobability complement of the non-fall event probability distribution(calculated by performed 1—the non-fall event probability distribution),or the probability complement of the non-fall event probabilitydistribution divided by the non-fall event probability distribution.

In other examples, the fall event probability distribution is obtainedby selecting a predetermined fall event probability distribution (e.g.from a number of options) based upon the non-fall event probabilitydistribution. This may comprise, for example, consulting a databasecorrelating predetermined fall event probability distributions topredetermined non-fall event probability distributions, and selectingthe fall event probability distribution that corresponds to apredetermined estimation of a probability distribution that most closelymatches (e.g. calculated using a cross-correlation technique or thelike) the generated non-fall event probability distribution.

Other methods of obtaining a fall event probability distribution using agenerated non-fall event probability distribution will be apparent tothe skilled person, the selection of which depends upon preferredimplementation details. Further examples are given in FIGS. 2 to 5 .

FIG. 2 illustrates an example non-fall event probability distribution200, also labelled P_(ADL), generated from a subject's movement dataover the predetermined data collection period, as described in step 140of method 100.

FIG. 3 illustrates an example fall event probability distribution 300,also labelled P_(D), obtained by taking the complement of the non-fallevent probability distribution 200. The fall event probabilitydistribution 300 has a high probability where the non-fall eventprobability distribution 200 has a low probability, and a lowprobability where the non-fall event probability distribution 200 has ahigh probability.

The accuracy of a fall event probability distribution obtained byprocessing a non-fall event probability distribution may be improved byrecognizing that there are some values for which the non-fall eventprobability is low that are unlikely to correspond to fall events.

FIG. 4 illustrates the example non-fall event probability distribution200, also labelled P_(ADL), alongside an example of a predeterminedestimation of a fall event probability distribution 400, also labelledP_(FALL). The predetermined estimation 400 may be generated usingsimulated fall data, or by some other method, such as from literature orhistoric measurements. The predetermined estimation may be obtained, forexample, from a database, storage or other memory module.

As can be seen from FIG. 4 , both the non-fall event probabilitydistribution 200 P_(ADL) and the predetermined estimation of a fallevent probability distribution 400, P_(FALL) have a low probability atvery low and very high values of the combination of the one or moreparameters (plotted along the x-axis). Referring back to FIG. 3 , thefall event probability distribution 300, obtained by reverting (i.e.computing the complement) the non-fall event probability distribution200, has a high probability at these values.

In an embodiment, only part of the non-fall event probabilitydistribution is processed to obtain the fall event probabilitydistribution, in order to exclude parts of the non-fall eventprobability distribution where a predetermined estimation of a fallevent probability distribution has a low probability. Which part of thenon-fall event probability distribution is processed is determined usingthe predetermined estimation of a fall event probability distribution.

FIG. 4 shows an example of how the part of the non-fall eventprobability distribution to be processed may be determined. In thisexample, a minimum value 410 for the predetermined estimation of a fallevent probability distribution 400 is chosen.

The minimum value 410 may be a percentage of the maximum probability ofthe predetermined estimation of a fall event probability distribution,or may be a predetermined value. The non-fall event probabilitydistribution 200 is only processed for values of the combination of theone or more parameters for which the predetermined estimation of a fallevent probability exceeds the minimum value 410.

In FIG. 4 , the part of the non-fall event probability distribution 200that is processed is the part between the boundaries 421 and 422. Thereare two boundaries 421 and 422 in FIG. 4 , but more complex probabilitydistribution in which more boundaries are required may be used.

FIG. 5 illustrates an example of a fall event probability distribution500 that may be obtained using this method. Between the boundaries 421and 422, the fall event probability distribution 500 is obtained bytaking the compliment of the part of the non-fall event probabilitydistribution 200 that lies between the boundaries 421 and 422.

The part of the fall event probability distribution 500 to the left of(lower) boundary 421 (i.e. having a lower value than the boundary 421)may be set as a constant equal to the fall event probability at boundary421, or may be given some predetermined value. A suitable predeterminedvalue may be the minimum value 410 used to determine which part of thenon-fall event probability distribution is processed, although othervalues are contemplated.

Similarly, the part of the fall event probability distribution 500 tothe right of (upper) boundary 422 (i.e. having a higher value than theboundary 422) may be set as a constant equal to the fall eventprobability at boundary 422, or given some predetermined value.

To save memory space, the fall event probability distribution may besimply stored as values between the lower 421 and upper 422 boundaries.During later processing, if an (x-axis) value lower than the lowerboundary 421 is used, the corresponding probability may be clipped tothe value at the lower boundary. Similarly, if an (x-axis) value higherthan the upper boundary is used, the corresponding probability may beclipped to the value at the upper boundary 422.

Other methods to generate the part of the fall event probabilitydistribution that is outside the boundaries of 421, 422 (i.e. define theprobability values outside of the boundaries 421, 422) may be used. Forexample, the part of the predetermined estimation of a fall eventprobability distribution 400 outside the boundaries 421 and 422 may beused as the part of the fall event probability distribution outside theboundaries 421 and 422.

Other methods may be used to determine which part of the non-fall eventprobability distribution is processed. For example, the part of thenon-fall event probability distribution may comprise the part of thenon-fall event probability distribution for values of the combination ofthe one or more parameters lying between the combination of the one ormore parameters having a maximum value in the non-fall event probabilitydistribution and the combination of the one or more parameters having amaximum value in the predetermined estimation of a fall eventprobability distribution.

In embodiments in which a plurality of non-fall event probabilitydistributions are generated in step 140, any previously described methodof generating a fall event probability distribution could be applied toeach of the plurality of non-fall event probability distributions(thereby generating intermediate fall event probability distributions,one for each non-fall event probability distribution). The intermediatefall event probability distributions may be combined, e.g. bycalculating the product or summing logs, in order to generate the(overall) fall event probability distribution.

Whilst presently illustrated using only two dimensions for the sake ofimproved clarity, in some embodiments, the non-fall event probabilitydistribution and/or fall event probability distribution may be a jointprobability density function, i.e. a multivariate probabilitydistribution. This can be formatted using a plurality of two-dimensionalprobability distributions (e.g. for each parameter or sub-combination ofparameters).

Different dimensions of the joint probability dimension function maytherefore represent a different variable from the movement data.Dimensions of the multi-dimensional probability distribution may then beprocessed independently to generate a corresponding dimension for thefall-event probability distribution (e.g. by performing any previouslydescribed method for generating a fall-event probability distribution).In other words, each of a plurality of two-dimensional probabilitydistributions forming the non-fall event probability distribution may beindividually processed using a previously described method (e.g.inverting or the like) to generate a two-dimensional probabilitydistribution for the fall event probability distribution. This forms amultivariate probability distribution for the fall event probabilitydistribution.

The two-dimensional probability distributions could be combined, e.g.using a product or summing logs, to generate a two-dimensional fallevent probability distribution. Alternatively, the fall eventprobability distribution may be maintained as a joint probabilitydensity function.

Referring back to FIG. 1 , at step 160, the method comprises obtaining athreshold value, for the fall event probability distribution, thatdistinguishes between a fall event and a non-fall event.

Step 160 may comprise processing the non-fall event probabilitydistribution and the fall event probability distribution in order tocalculate the threshold value. This processing is performed using one ormore computer-based algorithmic processes, examples of which are nowprovided.

In some example, the threshold value may be chosen such that theclassifier has a particular false alarm rate. This approach isparticularly suited when the fall event probability distribution iscalculated using a likelihood ratio. This method for determining thethreshold value recognizes that classifiers based on the likelihoodratio test provide optimal detection accuracy, according to theNeyman-Pearson theorem. For a given false alarm rate, the likelihoodratio test provides the lowest probability of incorrectly classifying afall event as a non-fall event.

In some embodiments, the threshold value may be personalized by settingit at the fall event probability at which the false alarm rate for thesubject is equal to a predetermined rate FA_set. The threshold value maybe determined from the subject-specific non-fall event probabilitydistribution, by using the fact that the integral of the non-fall eventprobability distribution over the region where the fall probability isabove the threshold value equals the predetermined false alarmprobability. This false alarm probability can be determined as thequotient between the predetermined false alarm rate FA_set and a triggerrate, where the trigger rate is defined as the number of detected eventsper unit time for the subject.

In some embodiments, only part of the non-fall event probabilitydistribution is used in the calculation to determine the threshold valuecorresponding to a predetermined false alarm rate. This part may bedetermined using any of the previously described methods. In this way, athreshold value set by integrating the non-fall event probabilitydistribution may yield comparable detection accuracy to a thresholdvalue determined from the likelihood ratio test.

Other embodiments may comprise obtaining a predetermined thresholdvalue, e.g. a standard threshold value used in the art.

In yet other embodiments, the threshold value may be set as the value atwhich the false alarm rate for the subject, defined as the rate at whichnon-fall events are determined to be fall events, is equal to apredetermined rate. In this way, the threshold value may be personalizedto the subject to ensure that the false alarm rate for that subject isat an acceptable level. For example, a user may be offered to set arequired (maximum) false alarm rate. This can be through the userinterface of a device, via a dedicated interface such as a web page orportal, or by an operator (care provider) using a system configurationtool. Several other methods are conceivable.

For example, the user may navigate through a graphical user interface toa screen displaying control settings, where a section, field or (soft)button is offered to set the required maximum false alarm rate. Insetting the false alarm rate, the user interface may output to the usera detection sensitivity expected at that setting. In other words, bysetting the false alarm rate, the detection sensitivity is changed. Afalse alarm rate of once per day guarantees high detection sensitivity,whereas the user may want a lower detection sensitivity at which thefalse alarm rate would amount to once per week or once per month even.

Optionally, when the user sets the accepted false alarm rate, the cancelfunction might be disabled, so the user is not burdened upon a detectedfall, and the alarm can be forwarded to a care provider faster (no needto await a cancel).

Instead of setting the required false alarm rate in absolute terms, itcan also be set in a relative manner. For example, the user requests toset the false alarm rate at half the rate that the user is currentlyexperiencing.

A preferred arrangement to determine the threshold value is as describedby step 160 above. Another approach, however, is to keep using thenon-fall event probability distribution and the fall event probabilitydistribution as they exist in the system. Possibly they are obtained inan earlier data collection effort, and were determined in step 140 and150 at that time, using those data. They can also have beenpre-installed by the provider/manufacturer of the fall detector. In thatcase they are typically obtained from several (volunteering) users andrepresents an average, or default distribution. In fact, as will beexplained below, the combined fall event probability distribution can bestored, instead of the individual non-fall event probabilitydistribution and fall event probability distribution.

The (existing) non-fall event probability distribution and fall eventprobability distribution are combined in a new fall event probabilitydistribution such that, given the originating distributions, a largerdetection probability is found at those parameter values at which theoriginating fall event probability relative to the non-fall eventprobability is larger. As mentioned above, the likelihood ratio is apreferred method to obtain this new fall event probability distribution.

Data are collected as before and parameter values are determined fromthem as before (step 130). In addition, the duration of the collectiontime span is determined.

In a next step, by using the obtained fall event probabilitydistribution, a non-fall detection rate function is determined from thecollected parameter values. This function takes a threshold value asinput and returns the false alarm rate that would happen at thatthreshold. The function can take the form of a table, for example. For agiven threshold, the number of collection parameter values (from thewhole set of collected values) is determined that has its fall eventprobability above the threshold. The number increases with decreasingthreshold. The false alarm rate follows as the quotient of this numberdivided by the duration of the collection time span. Alternatively, butmathematically equivalent, the fraction of collection parameters isdetermined that have their fall event probability above threshold, andthis fraction is multiplied with the trigger rate. The trigger rate isdetermined as the total number of parameter values in the collectiondivided by the duration of the collection time span.

In the final step, the threshold is determined at which the non-falldetection rate function is equal or less than the user-set false alarmrate.

In the above, given the set FA-rate that is being required, thethreshold has been determined from a non-fall detection rate function,the latter being obtained from the collected data. This process can bemade dynamic, by regularly recomputing the non-fall detection ratefunction and recomputing the corresponding threshold. Another approachwill be described next.

While described for application in a dynamic fashion, i.e. regularlyupdating the determined threshold, the method can also be applied in astatic manner, i.e. only determining the threshold once, or on everyuser request. The way of regular updating can be in several manners, asis known in the art. One possibility is to operate the system everyweek, using data of the past three months back from the updating moment.The approach can replace above method, although not preferred, by usinga standard, fixed or less frequently updated, non-fall detection ratefunction. Preferably, the approach is used in combination with abovemethod and is meant to provide a refined adaptation to that method.

FIG. 7 shows another embodiment where the false alarm rate (i.e. theFA-rate) 701 is set by a user. There is a non-fall detection ratefunction unit, or false alarm FA(θ) function 704. This function has beenprestored, using generic data from several volunteers, or has beenobtained as described in the above. The function can be stored as atable, for example.

The non-fall detection rate function FA(θ) function 704 has two outputs.The first output of the FA(θ) function 704 is a threshold 9 isdetermined (as explained above). The second output of the FA(θ) function704 is also used to derive a sensitivity factor, i.e.

${fact} = {- {\frac{d\theta}{dFA}.}}$

This sensitivity factor is determined in unit 705. The sensitivityfactor at a given threshold θ is found as the derivative

$- {\frac{d\theta}{dFA}.}$

In general, the function FA(θ) is stored in discrete form, such as atable. The derivative is computed according to methods as known in theart.

A detector 702 is configured to receive two inputs and based thereon tooutput the actual false alarm rate. Assuming the user has not beenfalling, this actual FA-rate as determined by the detector 702 is thequotient between the number of events the detector has declared to be afall and the duration of the period over which this number has beencounted. The duration should be long enough such that a sufficientnumber of counts will be collected. For example, a duration of threemonths could be taken when the set FA-rate 701 is about 1 per week(leading to about an expected dozen counts over 3 months).

Depending on the health state of the user, and depending on the setFA-rate 701 set by the user, it is fair to assume all events that thedetector has declared to be a fall are actually false alarms. However,if the user is a frequent faller and/or the FA-rate 701 is a low number,the assumption in the preceding sentence may not be valid. In that case,the determination of the actual FA-rate 702 needs to be refined. Forexample, the user is asked to confirm a genuine fall, or to indicate afalse alarm is a non-fall indeed. Instead of the user, another entity,for example a care provider, can provide this labelling. Another, lesspreferred, route is to assume a certain rate of genuine falls and toadjust the determined FA-rate accordingly.

The actual FA-rate 702 is compared with the set FA-rate 701 at thecomparator unit 703, where their difference ΔFA-rate is computed.

At multiplier unit 706 the difference ΔFA-rate is multiplied with thesensitivity factor

${{fact}{= {- \frac{d\theta}{dFA}}}},$

where the output is a (first order) estimate of a threshold change Δθ.

The threshold change Δθ is fed through a low pass filter (LPF) 707 as afirst input to a summation unit 708. The summation unit 708 alsoreceives a second input threshold θ (which is output from unit 704). Thesummation unit 708 takes the estimated threshold change Δθ as firstinput to adapt the threshold θ second input. The adapted threshold isthe output of the summation unit 708 that is used by the detector 702.

The determined FA-rate may exhibit fluctuations of considerable size,since the number of counts of non-fall events at the detector 702 willbe relatively low, or since otherwise extremely long collectiondurations would be needed. These fluctuations will lead to fluctuatingadaptations of the threshold θ, such that periods will happen duringwhich nearly no non-fall events will occur, but during which neitherfalls will be detected, supposed they happened, as well as periodsduring which the non-fall event rate will be large, and larger than thefalse alarm rate value 701 that the user has set at the user interface.To suppress such unstable behavior, the Low Pass Filter (LPF) 707 isintroduced. Other techniques as known in control theory can be appliedas well. The effect of the LPF 707 is to smooth the size and changes inthe adaptation of threshold θ.

Low pass filters are well known in digital signal processing. Aparticular, and simple, form is to multiply the obtained Δθ with anattenuation factor α: Δθ′=α·Δθ, where 0<α<1, for example 0.1 or 0.9. Astronger low-pass, i.e. smaller bandwidth respectively lower cut-offfrequency, or a lower a are to be selected when the set FA-rate WtK−1 islow or collection durations or short.

When the user requests the false alarm to change in relative terms, e.g.to half the FA-rate, the non-fall detection rate function FA(θ) function704 can also be used. It has been found from experimental data thatlog(FA(θ)˜θ. Therefore, halving (for example) the false alarm ratetranslates to adding a constant offset log(0.5) to the threshold θ

$\left( {{weighted}{with}{the}{proportionality}{constant}\frac{d\log{FA}}{d\theta}} \right).$

FIG. 6 illustrates a fall detection system 600 for detecting a fall of asubject, comprising one or more sensors 610 and a processing system 620,according to an embodiment of the invention. The processing system 620is, itself, an embodiment of the invention.

The one or more sensors 610 are configured to obtain movement data 615responsive to the subject's movement. The one or more sensors mayinclude at least one of an accelerometer, a barometric sensor, a tiltswitch, a vibration sensor and a gyroscope. The one or more sensors maybe part of a wearable device, for example, a smartwatch or a pendantworn around the neck.

The processing system 620 comprises one or more processors 621 adaptedto receive movement data 615 from the one or more sensors 610, and todetect events that occur in the movement data 615. An event may bedetected when a defined trigger parameter (which may be a combination ofparameters of the movement data) changes by more than a predeterminedamount or when a value of the trigger parameter breaches a predeterminedthreshold, as previously described. Other methods would be apparent tothe skilled person.

Event information 622 from events that occur during a predetermined datacollection period is stored in a memory 623. This event informationcomprises the values of a one or more parameters that may (together orindividually) indicate the probability of a fall at the time of theevent for each detected event, such as height, height change,orientation, orientation change, air pressure change, velocity,acceleration, duration and impact.

At the end of the predetermined data collection period, the one or moreprocessors 621 obtain the event information 622 from the memory 623, andgenerate a non-fall event probability distribution by calculating aprobability distribution from the event information. In particular, anon-fall event probability distribution is calculated for a combinationof the one or more parameters for which values are provided in the eventinformation. Preferably, the one or more parameters comprises aplurality of parameters.

The one or more processors 621 obtain a fall event probabilitydistribution for the combination of the one or more parameters using thenon-fall event probability distribution.

The one or more processors 621 also obtain a threshold value for thefall event probability distribution. This threshold value can be used todistinguish between fall events and non-fall events. The threshold valuemay be stored in the memory 623.

In some examples, the one or more processors are adapted to process thenon-fall event probability distribution and the fall event probabilitydistribution to determine a threshold value 624 for the fall eventprobability distribution, such that the threshold value 624 may be usedto distinguish between fall events and non-fall events. The non-fallevent probability distribution and the fall event probabilitydistribution may, for example, be processed using any of the describedmethods. The determined threshold value 624 is stored in the memory 623.

The memory 623 may store a predetermined estimation of a fall eventprobability distribution 625 which may be used by the one or moreprocessors 621 when determining the threshold value 624 and/or the fallevent probability distribution.

For events that are detected after the predetermined data collectionperiod has elapsed, the one or more processors 621 may compare the fallevent probability for the value of the combination of the one or moreparameters with the threshold value 624 and classify the event as a fallevent or a non-fall event on the basis of this comparison.

The fall detection system 600 may also comprise a user interface 630configured to provide a cancel function 635. The cancel function 635 maybe configured to re-classify a detected fall event as a non-fall eventwhen activated by the subject.

In some embodiments, the threshold value 624 corresponds to apredetermined false alarm rate. A false alarm probability can bedetermined as the quotient between the predetermined false alarm rateand a trigger rate, where the trigger rate is defined as the number ofdetected events per unit time for the subject. The false alarm rateand/or the false alarm probability may be sent to the user interface 630to inform the subject of the accuracy of the system.

In some embodiments, the user interface 630 is configured to receive auser instruction to disable the cancel function, should the subjectconsider the system to be accurate enough that a cancel function is notrequired. In other embodiments, the processing system 620 mayautomatically disable the cancel function if the determined false alarmprobability is below a predetermined value, for example, 0.001 or0.00001.

It will be understood that the disclosed methods arecomputer-implemented methods. As such, there is also proposed a conceptof a computer program comprising code means for implementing anydescribed method when said program is run on a processing system.

The skilled person would be readily capable of developing a processorfor carrying out any herein described method. Thus, each step of a flowchart may represent a different action performed by a processor, and maybe performed by a respective module of the processing processor.

As discussed above, the system makes use of a processor to perform thedata processing. The processor can be implemented in numerous ways, withsoftware and/or hardware, to perform the various functions required. Theprocessor typically employs one or more microprocessors that may beprogrammed using software (e.g. microcode) to perform the requiredfunctions. The processor may be implemented as a combination ofdedicated hardware to perform some functions and one or more programmedmicroprocessors and associated circuitry to perform other functions.

Examples of circuitry that may be employed in various embodiments of thepresent disclosure include, but are not limited to, conventionalmicroprocessors, application specific integrated circuits (ASICs), andfield-programmable gate arrays (FPGAs).

In various implementations, the processor may be associated with one ormore storage media such as volatile and non-volatile computer memorysuch as RAM, PROM, EPROM, and EEPROM. The storage media may be encodedwith one or more programs that, when executed on one or more processorsand/or controllers, perform the required functions. Various storagemedia may be fixed within a processor or controller or may betransportable, such that the one or more programs stored thereon can beloaded into a processor.

Variations to the disclosed embodiments can be understood and effectedby those skilled in the art in practicing the claimed invention, from astudy of the drawings, the disclosure and the appended claims. In theclaims, the word “comprising” does not exclude other elements or steps,and the indefinite article “a” or “an” does not exclude a plurality. Asingle processor or other unit may fulfill the functions of severalitems recited in the claims. The mere fact that certain measures arerecited in mutually different dependent claims does not indicate that acombination of these measures cannot be used to advantage. A computerprogram may be stored/distributed on a suitable medium, such as anoptical storage medium or a solid-state medium supplied together with oras part of other hardware, but may also be distributed in other forms,such as via the Internet or other wired or wireless telecommunicationsystems. If the term “adapted to” is used in the claims or description,it is noted that the term “adapted to” is intended to be equivalent tothe term “configured to”. Any reference signs in the claims should notbe construed as limiting the scope.

1. A computer-based method of adapting a classifier to distinguishbetween a fall event and a non-fall event of a subject, thecomputer-based method comprising: obtaining movement data responsive tothe subject's movement during everyday activities over a predetermineddata collection period; detecting one or more events in the movementdata; obtaining a value for each of a one or more parameters from themovement data at the time of the event for each event that occurs duringthe predetermined data collection period; obtaining a fall eventprobability distribution that is predetermined or based on a combinationof the one or more parameters; determining a non-fall detection ratefunction based on the obtained fall event probability distribution;determining a threshold value from the non-fall detection rate function,where the non-fall detection rate is below a preset value.
 2. Thecomputer-implemented method of claim 1, wherein the step of obtaining afall event probability distribution is based on a combination of the oneor more parameters using the obtained values for each of the one or moreparameters; and the step of determining a non-fall detecting ratefunction is based on the obtained fall event probability distributionand the obtained values for each of the one or more parameters.
 3. Thecomputer-implemented method of claim 1, wherein the step of determininga non-fall detection rate function also takes into account the durationof a predetermined data collection period.
 4. The computer-based methodof claim 1, wherein the step of determining the threshold value isadapted based on a difference or ratio between a set false alarm rateand an observed false alarm rate.
 5. The computer-based method of claim1, wherein the step of determining the threshold value is adapted basedon the relative difference between current false alarm rate and a setfalse alarm rate.
 6. A fall detector system for detecting a fall of asubject, the system comprising: a user interface having an input forenabling a user to set a false alarm rate reference value; a thresholddetermination subsystem is configured to execute the computer-basedmethod of claim 1 for determining a threshold value corresponding to thefalse alarm rate reference value; and wherein the fall detector isconfigured to use the determined threshold.
 7. The fall detector systemof claim 6, wherein the false alarm rate reference value that is enteredby the user is relative to an existing value.
 8. A method for training afall detection classifier to distinguish between fall events andnon-fall events for a user by using subject-specific movement data, themethod comprising: setting a preferred non-fall detection rate for theuser; obtaining the subject-specific movement data by monitoring asubject's movements during everyday activities over a predetermined datacollection period, wherein for each detected event in the movement data,values for one or more parameters indicating a fall are obtained;generating a subject-specific non-fall detection rate function based onthe obtained values; and determining a threshold value using thenon-fall detection rate function and the non-fall detection rate set bythe user to distinguish fall events from non-fall events.